def test_evaluate_population(self):
protogene = ProtoGene(IntGene, 'a', min_value=0, max_value=100)
protogenome = ProtoGenome([protogene])
algo = GeneticAlgorithm(protogenome, fitness_evaluator, termination_criteria=convergence_stop)
algo.initialize()
algo.evaluate_population()
for individual in algo.population.individuals:
assert individual.score == fitness_evaluator(individual)
def test_evolve_population(self):
protogene = ProtoGene(IntGene, 'a', min_value=0, max_value=10)
protogenome = ProtoGenome([protogene])
algo = GeneticAlgorithm(protogenome, fitness_evaluator, termination_criteria=convergence_stop)
algo.initialize()
algo.evaluate_population()
g1 = algo.generation
algo.evolve_population()
g2 = algo.generation
assert g1 == 0 and g2 == 1
def test_best_individual(self):
protogene = ProtoGene(IntGene, 'a', min_value=0, max_value=100)
protogenome = ProtoGenome([protogene])
algo = GeneticAlgorithm(protogenome, fitness_evaluator, termination_criteria=convergence_stop)
algo.initialize()
for i, individual in enumerate(algo.population.individuals):
individual.set_value('a', i)
algo.evaluate_population()
bi = algo.best_individual()
assert bi.score == 0
def test_should_terminate(self):
protogene = ProtoGene(IntGene, 'a', min_value=0, max_value=100, value=1)
protogenome = ProtoGenome([protogene], mutation_probability=1)
algo = GeneticAlgorithm(protogenome, fitness_evaluator, termination_criteria=convergence_stop)
algo.initialize()
for individual in algo.population.individuals:
individual.score = 1
for x in range(11):
stats = {'min_score' : 0}
algo.population_stats.append(stats)
algo.generation = 10
st = algo.should_terminate()
assert st == True
algo.generation = 1
st = algo.should_terminate()
assert st == False
def test_initialize(self):
algo = GeneticAlgorithm(self.protogenome, fitness_evaluator)
algo.initialize()
